{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing General Dynamical Systems\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_building/customize_dynamical_systems.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_building/customize_dynamical_systems.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) @[Chaoming Wang](mailto:adaduo@outlook.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous sections have shown how to build neuron models, synapse models, and network models. In fact, these brain objects all inherit the base class [brainpy.DynamicalSystem](../apis/auto/dyn/generated/brainpy.dyn.base.DynamicalSystem.rst), ``brainpy.DynamicalSystem`` is the universal language to define dynamical models in BrainPy.\n",
    "\n",
    "To begin with, let's make a brief summary of previous dynamic models and give the definition of a dynamical system."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.395266Z",
     "start_time": "2025-10-06T03:48:46.480433Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bm.set_platform('cpu')\n",
    "\n",
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is a dynamical system?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking back to the neuron and synapse models defined in the previous sections, they share a common feature that **they all contain some variables that change over time**. Because of these variables, the models become 'dynamic' and behave differently at different times.\n",
    "\n",
    "Actually, a *dynamical system* is defined as a system with time-dependent states. These time-dependent states are displayed as variables in the previous models.\n",
    "\n",
    "Mathematically, the change of a state $X$ can be expressed as\n",
    "\n",
    "$$\n",
    "\\dot{X} = f(X, t)\n",
    "$$\n",
    "\n",
    "where $X$ is the state of the system, $t$ is the time, and $f$ is a function describing the time dependence of the state. \n",
    "\n",
    "Alternatively, the evolution of the system over time can be given by\n",
    "\n",
    "$$\n",
    "X(t+dt) = F\\left(X(t), t, dt\\right)\n",
    "$$\n",
    "\n",
    "where $dt$ is the time step and $F$ is the evolution rule to update the system's state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing your dynamical systems\n",
    "\n",
    "According to the mathematical expression of a dynamical system, any subclass of ``brainpy.DynamicalSystem`` must implement an updating rule in the ``update(self, tdi)`` function.\n",
    "\n",
    "To define a dynamical system, the following requirements should be satisfied:\n",
    "- Inherit from `brainpy.DynamicalSystem`.\n",
    "- Implement the `update(self, tdi)` function.\n",
    "- When defining variables, they should be declared as `brainpy.math.Variable`.\n",
    "- When updating the variables, it should be realized by [in-place operations](./tutorial_basics/arrays_and_variables.ipynb).\n",
    "\n",
    "Below is a simple example of a dynamical system."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.407961Z",
     "start_time": "2025-10-06T03:48:50.398886Z"
    }
   },
   "source": [
    "class FitzHughNagumoModel(bp.DynamicalSystem):\n",
    "    def __init__(self, a=0.8, b=0.7, tau=12.5, **kwargs):\n",
    "        super(FitzHughNagumoModel, self).__init__(**kwargs)\n",
    "        \n",
    "        # parameters\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.tau = tau\n",
    "        \n",
    "        # variables should be packed by brainpy.math.Variable\n",
    "        self.v = bm.Variable([0.])\n",
    "        self.w = bm.Variable([0.])\n",
    "        self.I = bm.Variable([0.])\n",
    "        \n",
    "    def update(self, tdi):\n",
    "        t, dt = tdi.t, tdi.dt\n",
    "        # t : the current time, the system keyword\n",
    "        # dt : the time step, the system keyword\n",
    "        \n",
    "        # in-place update\n",
    "        self.w += (self.v + self.a - self.b * self.w) / self.tau * dt\n",
    "        self.v += (self.v - self.v ** 3 / 3 - self.w + self.I) * dt\n",
    "        self.I[:] = 0."
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have defined a dynamical system called [FitzHugh–Nagumo neuron model](https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model), whose dynamics is given by: \n",
    "\n",
    "$$\n",
    "{\\dot {v}}=v-{\\frac {v^{3}}{3}}-w+I, \\\\\n",
    "\\tau {\\dot {w}}=v+a-bw.\n",
    "$$\n",
    "\n",
    "By using the [Euler method](../apis/integrators/generated/brainpy.integrators.ode.explicit_rk.Euler.rst), this system can be updated by the following rule:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "v(t+dt) &= v(t) + [v(t)-{v(t)^{3}/3}-w(t)+RI] * dt, \\\\\n",
    "w(t + dt) &= w(t) + [v(t) + a - b w(t)] * dt.\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Advantages of using `brainpy.DynamicalSystem`"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "There are several advantages of defining a dynamical system as `brainpy.DynamicalSystem`."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. A systematic naming system. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, every instance of ``DynamicalSystem`` has its unique name."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.849489Z",
     "start_time": "2025-10-06T03:48:50.832862Z"
    }
   },
   "source": [
    "fhn = FitzHughNagumoModel()"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.867518Z",
     "start_time": "2025-10-06T03:48:50.863719Z"
    }
   },
   "source": [
    "fhn.name  # name for \"fhn\" instance"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FitzHughNagumoModel0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every instance has its unique name:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.903476Z",
     "start_time": "2025-10-06T03:48:50.895433Z"
    }
   },
   "source": [
    "for _ in range(3):\n",
    "    print(FitzHughNagumoModel().name)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FitzHughNagumoModel1\n",
      "FitzHughNagumoModel2\n",
      "FitzHughNagumoModel3\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users can also specify the name of a dynamic system:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.923226Z",
     "start_time": "2025-10-06T03:48:50.918227Z"
    }
   },
   "source": [
    "fhn2 = FitzHughNagumoModel(name='X')\n",
    "\n",
    "fhn2.name"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.961017Z",
     "start_time": "2025-10-06T03:48:50.956304Z"
    }
   },
   "source": [
    "# same name will cause error\n",
    "\n",
    "try:\n",
    "    FitzHughNagumoModel(name='X')\n",
    "except bp.errors.UniqueNameError as e:\n",
    "    print(e)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In BrainPy, each object should have a unique name. However, we detect that None(mode=NonBatchingMode) has a used name \"X\". \n",
      "If you try to run multiple trials, you may need \n",
      "\n",
      ">>> brainpy.math.clear_name_cache() \n",
      "\n",
      "to clear all cached names. \n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, variables, children nodes, etc. inside an instance can be easily accessed by their *absolute* or *relative* path. "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:50.991836Z",
     "start_time": "2025-10-06T03:48:50.986190Z"
    }
   },
   "source": [
    "# All variables can be acessed by \n",
    "# 1). the absolute path\n",
    "\n",
    "fhn2.vars()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X.v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'X.w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'X.I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " )}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.040088Z",
     "start_time": "2025-10-06T03:48:51.034949Z"
    }
   },
   "source": [
    "# 2). or, the relative path\n",
    "\n",
    "fhn2.vars(method='relative')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " )}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Convenient operations for simulation and analysis.\n",
    "Brainpy provides different runners for dynamics simulation and analyzers for dynamics analysis, both of which require the dynamic model to be `Brainpy.DynamicalSystem`. For example, dynamic models can be packed by a runner for simulation:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.431966Z",
     "start_time": "2025-10-06T03:48:51.056286Z"
    }
   },
   "source": [
    "runner = bp.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5))\n",
    "runner(duration=100)\n",
    "\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
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       "<Figure size 640x480 with 1 Axes>"
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      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please see [Runners](../tutorial_toolbox/runners.ipynb) to know more about the operations in runners."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Efficient computation.\n",
    "\n",
    "``brainpy.DynamicalSystem`` is a subclass of [brainpy.BrainPyObject](../apis/generated/brainpy.base.BrainPyObject.rst), and therefore, any instance of ``brainpy.DynamicalSystem`` can be complied [just-in-time](../tutorial_basics/jit_compilation.ipynb) into efficient machine codes targeting on CPUs, GPUs, and TPUs."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.603121Z",
     "start_time": "2025-10-06T03:48:51.440498Z"
    }
   },
   "source": [
    "runner = bp.DSRunner(fhn2, monitors=['v', 'w'], inputs=('I', 1.5), jit=True)\n",
    "runner(duration=100)\n",
    "\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon.v, legend='v', show=False)\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w', show=True)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "74da90927f0e4d1182215d5f630dea4d"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Support composable programming.\n",
    "Instances of ``brainpy.DynamicalSystem`` can be combined at will. The combined system is also a `brainpy.DynamicalSystem` and enjoys all the properties, methods, and interfaces provided by `brainpy.DynamicalSystem`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "For example, if the instances are wrapped into a container, i.e. `brainpy.Network`, variables and nodes can also be accessed by their absolute or relative path."
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.613346Z",
     "start_time": "2025-10-06T03:48:51.608252Z"
    }
   },
   "source": "fhn_net = bp.Network(f1=fhn, f2=fhn2)",
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.637408Z",
     "start_time": "2025-10-06T03:48:51.631226Z"
    }
   },
   "source": [
    "# absolute access of variables\n",
    "\n",
    "fhn_net.vars()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FitzHughNagumoModel0.v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'FitzHughNagumoModel0.w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'FitzHughNagumoModel0.I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'X.v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'X.w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'X.I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " )}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.658951Z",
     "start_time": "2025-10-06T03:48:51.653756Z"
    }
   },
   "source": [
    "# relative access of variables\n",
    "\n",
    "fhn_net.vars(method='relative')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'children.f1.v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'children.f1.w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'children.f1.I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'children.f2.v': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'children.f2.w': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " ),\n",
       " 'children.f2.I': Variable(\n",
       "   value=[\n",
       "     ShapedArray(float32[], weak_type=True)\n",
       "   ],\n",
       "   _batch_axis=None,\n",
       "   axis_names=None\n",
       " )}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.680761Z",
     "start_time": "2025-10-06T03:48:51.676005Z"
    }
   },
   "source": [
    "# absolute access of nodes\n",
    "\n",
    "fhn_net.nodes()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DynSysGroup0': DynSysGroup0(mode=NonBatchingMode),\n",
       " 'FitzHughNagumoModel0': FitzHughNagumoModel0(mode=NonBatchingMode),\n",
       " 'X': X(mode=NonBatchingMode)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:48:51.703465Z",
     "start_time": "2025-10-06T03:48:51.699297Z"
    }
   },
   "source": [
    "# relative access of nodes\n",
    "\n",
    "fhn_net.nodes(method='relative')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': DynSysGroup0(mode=NonBatchingMode),\n",
       " 'children.f1': FitzHughNagumoModel0(mode=NonBatchingMode),\n",
       " 'children.f2': X(mode=NonBatchingMode)}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:49:26.199246Z",
     "start_time": "2025-10-06T03:49:26.024111Z"
    }
   },
   "source": [
    "runner = bp.DSRunner(fhn_net,\n",
    "                         monitors=['f1.v', 'f2.v'],\n",
    "                         inputs=[('f1.I', 1.5),   # relative access to variable \"I\" in 'fhn1'\n",
    "                                 ('f2.I', 1.0),])  # absolute access to variable \"I\" in 'fhn2'\n",
    "runner(duration=100)\n",
    "\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon['f1.v'], legend='fhn1.v', show=False)\n",
    "bp.visualize.line_plot(runner.mon.ts, runner.mon['f2.v'], legend='fhn2.v', show=True)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "23cfc8954691495090ac9cf73f4addfc"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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     "jetTransient": {
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    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\adadu\\\\miniconda3\\\\envs\\\\bdp\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
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